Python实现CSV转嵌套字典（不使用Pandas）

Problem #

I have a CSV file and want to process it into a nested dictionary grouped by column values. The CSV format is as follows:

sample, date, depth, analyte, result
'ABC', '01/01/2018', '3', 'LEAD', 0.22
'ABC', '02/01/2018', '3', 'LEAD', 0.25
'ABC', '01/01/2018', '5', 'LEAD', 0.19
'ABC', '02/01/2018', '5', 'LEAD', 0.18
'ABC', '01/01/2018', '3', 'MERCURY', 0.97
'ABC', '02/01/2018', '3', 'MERCURY', 0.95
'ABC', '01/01/2018', '5', 'MERCURY', 0.34
'ABC', '02/01/2018', '5', 'MERCURY', 0.11
'DEF', '01/01/2018', '3', 'LEAD', 0.07
'DEF', '02/01/2018', '3', 'LEAD', 0.04
'DEF', '01/01/2018', '5', 'LEAD', 0.16
'DEF', '02/01/2018', '5', 'LEAD', 0.65
'DEF', '01/01/2018', '3', 'MERCURY', 0.03
'DEF', '02/01/2018', '3', 'MERCURY', 0.01
'DEF', '01/01/2018', '5', 'MERCURY', 0.11
'DEF', '02/01/2018', '5', 'MERCURY', 0.13

I want the final dictionary structure to be:

dictionary = {sample: {date: {depth: [analyte, result], [analyte, result] ... }}}

I need to access unique result sets like dictionary['ABC']['01/01/2018']['5'], which should return [['LEAD', 0.19], ['MERCURY', 0.34]]. I want to avoid Pandas and need a Pythonic solution; as a beginner, nested loops got confusing, so I'm seeking help.

Solution #

No worries—we can use Python's built-in csv module and collections.defaultdict to handle this nested structure cleanly, without messy conditional checks. Let's break this down:

Step 1: Import Required Modules

We'll use csv to read the file and defaultdict to simplify building the nested dictionary:

import csv
from collections import defaultdict

Step 2: Build the Nested Structure

The key here is using a 3-level nested defaultdict where each missing key automatically creates the next level. The innermost level is a list that we can append our analyte-result pairs to directly:

def build_nested_csv_data(csv_file_path):
    # Create a nested structure: sample -> date -> depth -> list of [analyte, result]
    nested_data = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
    
    with open(csv_file_path, 'r') as file:
        # Read CSV rows as dictionaries (access values by column name)
        reader = csv.DictReader(file)
        
        for row in reader:
            # Clean up values (remove surrounding single quotes)
            sample = row['sample'].strip("'")
            date = row['date'].strip("'")
            depth = row['depth'].strip("'")
            analyte = row['analyte'].strip("'")
            # Convert result string to float
            result = float(row['result'])
            
            # Append the analyte-result pair to the correct nested list
            nested_data[sample][date][depth].append([analyte, result])
    
    # Optional: Convert defaultdicts to regular dicts if preferred
    def convert_to_regular_dict(d):
        if isinstance(d, defaultdict):
            return {key: convert_to_regular_dict(value) for key, value in d.items()}
        return d
    
    return convert_to_regular_dict(nested_data)

Step 3: Use the Function

Call the function with your CSV file path, then access the data exactly as you wanted:

# Replace 'your_data.csv' with your actual file path
final_dict = build_nested_csv_data('your_data.csv')

# Example access
print(final_dict['ABC']['01/01/2018']['5'])
# Output: [['LEAD', 0.19], ['MERCURY', 0.34]]

Explanation

• csv.DictReader: Makes it easy to reference columns by name (like row['sample']) instead of index, which makes the code readable and less error-prone.
• defaultdict: Eliminates the need to manually check if a key exists at each level (no more if sample not in data: data[sample] = {}). It automatically creates the missing level when you access it.
• Cleaning Values: The .strip("'") removes the single quotes from your CSV string values (e.g., 'ABC' becomes ABC).
• Optional Conversion: The convert_to_regular_dict function turns the defaultdict structure into standard Python dicts if you don't want the auto-creation behavior anymore.

Alternative: Without defaultdict (For Learning)

If you want to see how this works with regular dicts (to understand the explicit checks), here's a version that does the same thing without defaultdict:

def build_nested_data_no_defaultdict(csv_file_path):
    data = {}
    
    with open(csv_file_path, 'r') as file:
        reader = csv.DictReader(file)
        for row in reader:
            sample = row['sample'].strip("'")
            date = row['date'].strip("'")
            depth = row['depth'].strip("'")
            analyte = row['analyte'].strip("'")
            result = float(row['result'])
            
            # Check and create each level if missing
            if sample not in data:
                data[sample] = {}
            if date not in data[sample]:
                data[sample][date] = {}
            if depth not in data[sample][date]:
                data[sample][date][depth] = []
            
            # Append the pair
            data[sample][date][depth].append([analyte, result])
    
    return data

This is what you might have tried initially, but the defaultdict version is much cleaner and more Pythonic.

内容的提问来源于stack exchange，提问作者Ian B